This invention relates to a method, a system and a device for processing electrocardiac signals, or other biological (bio) signals, to reduce or eliminate baseline drift.
An electrocardiogram (ECG or EKG), is a graphic produced by an electrocardiograph, which records the electrical activity (a signal) of the heart over time. Electrical waves cause the heart muscle to pump. These waves pass through the body and can be measured at electrodes (electrical contacts) attached to the skin. Electrodes on different sides of the heart measure the activity of different parts of the heart muscle. An EKG displays the voltage (a signal) between pairs of these electrodes, and the muscle activity that they measure, from different directions. This display indicates the overall rhythm of the heart, and weaknesses in different parts of the heart muscle. It is a way to measure and diagnose abnormal rhythms of the heart, particularly abnormal rhythms caused by damage to the conductive tissue that carries electrical signals, or abnormal rhythms caused by levels of salts, such as potassium, that are too high or low.
The ability to analyze an EKG signal and detect variances therein, allows for monitoring the physiological condition of a heart. For instance, accurate detection of variances in an EKG signal allows for the detection of heart events, such as heartbeat detection, arrhythmias, ischemias, and a myriad of other events. To detect variances in the EKG signal, it is necessary to minimize or eliminate noise, which also causes variances in an EKG signal, but does not correspond to a physiological event of the heart. Otherwise, the noise variance may be misconstrued as a heart event. In turn, this can lead to a potential misdiagnosis, false positive, missed event, or failure to detect other rhythms, among other undesirable results.
Baseline drift is a type of noise that causes signals, such as an EKG signal, to wander, i.e., drift in a linear or nonlinear fashion. Baseline drift may arise from any number of factors including, but not limited to, drift in electronic signal conditioning, thermal or mechanical stresses at the electrodes, and changes in operation condition, e.g., variations in ambient or body temperature, patient movement, etc.
Present techniques used to minimize baseline drift involve the use of filters. For example, one known method of removing baseline drift involves the use of a high-pass filter to filter out frequencies below a selected cutoff frequency. High pass filters, however, deviate from their ideal models, resulting in undesirable performance. In particular, high pass filters feature “roll-off”, which refers to imperfections in the signal response of a digital filter around a cut-off value.
Digital filters attempt to approximate the desired ideal response by increasing the length (order) of their impulse response. Because the digital filters must be causal, delay of the output is a necessary result. The main tradeoffs in digital filters include additional delay and increased numeric precision required as the order of the filter is increased. Therefore, the roll-off is unavoidable as well as a significant delay in the signal when close approximations are required.
FIG. 1 shows a conventional high pass filter response curve 10. An ideal high pass response 5 features a sharp transition from low to high at a cutoff frequency f0. Roll-off is illustrated at a location 12 near the cutoff frequency f0. As shown, the conventional high pass filter allows signals of slightly lower frequency than the cutoff frequency f0 in addition to blocking signals of slightly higher frequency than the cutoff.
FIG. 2 shows a comparison between a conventional high pass response curve 14a and a response curve 14b of a conventional high pass finite impulse response (FIR) digital filter of length 63, in which the filter impulse response has been made longer in an attempt to approximate the ideal response 5. Although the response 14b more closely approximates the ideal response 5 than the response 14a, the response 14b delays the output by a factor of 2. If a lower order filter is needed, it is possible to introduce ripple in the pass band and stop band to increase the slope of the transition region. However, this also introduces other non-ideal response characteristics.
Based on the above descriptions of conventional high pass filters, it can be seen that any realizable high pass filters cannot fully attenuate low frequency noise such as baseline drift in EKG signals (or other bio signals, such as from the brain). The technical problem of baseline drift and other such frequency based noise in a bio signal is potentially extremely serious. This is particularly so when experienced in the field of, for example, EKG signals since errors occurring in readings of an EKG signal can lead to erroneous deductions as to patient condition or required treatments.